High-performance computing relies on performance-oriented infrastructures with access to
powerful computing resources to complete tasks that contribute to solve complex problems …

Training large AI models on numerous GPUs consumes a massive amount of energy,
making power delivery one of the largest limiting factors in building and operating …

As research and deployment of AI grows, the computational burden to support and sustain
its progress inevitably does too. To train or fine-tune state-of-the-art models in NLP …

Efficient resource scheduling in heterogeneous graphics processing unit (GPU) clusters are
critical for maximizing system performance and optimizing resource utilization. However …

Within the computing continuum, SBCs (single-board computers) are essential in the Edge
and Fog, with many featuring multiple processing cores and GPU accelerators. In this way …

Abstract Intelligence Processing Units (IPU) have proven useful for many AI applications. In
this paper, we evaluate them within the emerging field of AI for simulation, where traditional …

Background Mammography for the diagnosis of early breast cancer (BC) relies heavily on
the identification of breast masses. However, in the early stages, it might be challenging to …

Energy efficiency of software is crucial in minimizing environmental impact and reducing
operational costs of ICT systems. Energy efficiency is therefore a key area of contemporary …

Over the last decade both capital costs for the acquisition of modern HPC systems as well as
costs to power and cool these systems have increased significantly. In addition to direct …

The recent growth of Artificial Intelligence (AI), particularly large language models, requires
energy-demanding high-performance computing (HPC) data centers, which poses a …